a glowworm optimization method for the design of web services
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I.J. Intelligent Systems and Applications, 2012, 10, 89-102
Published Online September 2012 in MECS (http://www.mecs -press.org/)
DOI: 10.5815/ijisa.2012.10.10
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
A Glowworm Optimization Method for the
Design of Web Services
Koffka Khan
Department of Computing and Information Technology, The University of the West Indies, St. Augustine Campus @
TRINIDAD
Email: koffka.khan@sta.uwi.edu
Ashok Sahai
Department of Mathematics and Statistics, The University of the West Indies, St. Augustine Campus @ TRINIDAD
Email:Ashok.sahai@sta.uwi.edu
Abstract— A method for adaptive usability evaluation
of B2C eCommerce web services is proposed. For
measuring eCommerce usability a checklist integrating
eCommerce quality and usability is developed. By a
Glowworm swarm optimizat ion (GSO) neural
networks-based model the usability dimensions and
their checklist items are adaptively selected. A case
study for usability evaluation of an eCommerce
anthurium retail website is carried out. The
experimental results show that GSO with neural
networks supports the allocation of usability problems
and the defining of relevant improvement measures.
The main advantage of the approach is the adaptive
selection of most significant checklist dimensions and
items and thus significant reduction of the time for
usability evaluation and design.
Index Terms— eCommerce, Usability, Glowworm
swarm optimization, Neural Networks
I. Introduction
The concept of usability and Human-Computer
Interaction (HCI) design principles of visibility,
structure, simplicity, feedback, and so on are generic in
nature and focus on the design of effective user-system
interaction. In HCI literature [31] research into the
success or failure of B2C eCommerce has primarily
focused on the usability of the core Web site. Central to
this has been how design criteria such as ease of
navigation, optimal response time, and appropriate
content can be managed to create usable customer-
focused eCommerce sites [28].
From an online perspective, electronic commerce
provides the capability of buying and selling products
and informat ion on the Internet and other online
services [20]. Customers must perceive eCommerce as
effective and efficient for online retailing to be viable.
There are many types of eCommerce but the main focus
of this study is Business-to-Consumer (B2C)
eCommerce. Th is refers to electronic business
transactions between businesses and individual
consumers who are buyers [26].
A web presence and low prices were believed to be
the key drivers of success in early online retailing. More
recently beyond having a simple online presence and
low prices, service has become essential for creating
customer loyalty and improving customer satisfaction
[46], [50]. Service quality is generally defined as the
difference between expected service and perceived
service [15], [32]. The delivery of superior service
positively affects customers' perceived service quality
and subsequently increases a firm's profitability [23].
While extensive research has been conducted on service
quality in traditional retailing settings [38] service
quality in online retailing is a relat ively new topic.
Traditional service quality deals with the quality of
service based on human interactions and experiences in
non-web-based settings. However, online service
quality deals with interactions between humans and
technology. The way people perceive service quality in
web-based settings differs from service quality in non-
web-based settings because the acceptance and usage of
technologies differ among customers with different
beliefs about technology [33].
As online retailing grows, service quality has become
an increasingly important factor in determin ing the
success or failure of online retail businesses by
influencing consumers' online shopping experiences
[46]. Empirical ev idence shows that poor service
quality negatively affects online retailers such that over
60 percent of online shoppers exit prior to completion
of the transaction due to factors such as distrust of
shopping and handling charges [41]. Incomplete
product information (e.g. missing links and non-
working buttons) leads to customer frustration and in
turn to exiting. Given the difficu lties related to the
acquisition of customers in online retailing, it is crucial
for online retailers to retain customers [36]. Nonetheless,
some online retailers lose a valuable opportunity to
build loyalty because of poor service quality [43].
Many studies provide useful insights about key
dimensions of online service quality based on subjective
consumer perceptions and evaluations: WebQual [1],
90 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
WebQualTM [25], E-Qual [21], AST [7], SITEQUAL
[48], e-SQ [50], eTailQ [44], and eTransQual [2].
Empirical research data has shown that e-SQ affects
customers‟ satisfaction, intent to purchase and purchase
and is the key driver of repeat purchases from Web sites.
Key success factors for any online business, such as,
repeat purchases, customer loyalty and eventually
profitability will not be achieved unless their customers‟
service quality needs are satisfied.
This paper has a focus on the consumer shopping
process in the context of eCommerce retailing. Zeithaml
[49] defines eService quality (e-SQ) as “the extent to
which a web site facilitates efficient and effective
shopping, purchasing, and delivery.”
Studies show that usability is a crucial success factor
in eCommerce. Usability can be applied to specific
contexts with the use of computer systems. Usability
measures the quality of a user's experience when
interacting with a p roduct or system, for example, a
Web site. Measuring usability is particularly difficu lt
because usability is not a unidimensional product or
user characteristic, but emerges as a multid imensional
characteristic in the context o f users performing tasks
with a product in a specific environment. Users of
eCommerce environments interact with the customer
(or consumer) interface of the eCommerce sites to
conduct transactions [10]. The consumer interface is
different from a conventional user interface that focuses
mainly on the task of conveying information in a
cognitively efficient way, facilitating ease of use and
ease of learning of the computer system. Conversely, a
customer interface should contain elements that attract a
visitor to stay and become a customer, and also return
for repeat business.
ECommerce-specific design guidelines focus on the
design characteristics of the Web site, such as, its
navigation, structure, homepage design and page layout.
In the retail eCommerce environment, usability is
concerned with the pragmat ics of how a customer
perceives and interacts with the Web site. Ease of
navigation and appropriateness of design are some of
the aspects that will influence the customer‟s judgments
regarding the usability of the webstore.
„Suitability for the task‟ is a key aspect when
considering the usability of a system. In this paper, the
eCommerce system must support the customers in the
tasks they are trying to accomplish. [3], regarded
usability as "a property of the overall system: it is the
quality of use in a context." The goals of the users, that
is, what the users are trying to do with the system are
very important concepts related to the usability of any
system. The system should support what the users are
trying to accomplish in an efficient, effective and
satisfying way.
In the area o f online retailing the design of websites
that are efficient and effective has become key factors
in determining the success of online companies. The
term quality is used to encompass these concepts.
Quality in the traditional retail setting has gone on for
decades, while quality in the online retail setting is
relatively new. eCommerce usability is regarded as
ensuring that interactive products, such as eCommerce
applications, are easy to learn, effective to use and
enjoyable from the user‟s perspective and involves the
optimization of user interaction with these interactive
products [34].
Recently discussions recur on which measures of
usability are suitable and on how to understand the
relation between different measures of usability [17]. [3]
regarded usability as "a property of the overall system:
it is the quality of use in a context." Users of
eCommerce environments interact with the customer
(or consumer) interface of the eCommerce sites to
conduct transactions [10]. A customer interface should
contain elements that attract a visitor to stay and
become a customer, and also return for repeat business.
There is a need of an approach for eCommerce
usability evaluation integrating quality and usability
measures of eCommerce. In this paper such method is
proposed. It uses a neural networks model for
eCommerce usability evaluation and design.
II. GSO Method
A. Existing problem and solution
We propose a method for eCommerce usability
evaluation and design (GSO) based on neural networks
integrating usability and quality measures of
eCommerce systems.
Usability evaluation aims at weaknesses of a system
and gives hints for improving its usability. Most
usability evaluations gather both objective and
subjective quantitative data in the context of realistic
scenarios-of-use.
Objective data are measures of part icipants'
performance.
Subjective data are measures of participants'
opinions or attitudes concerning their perception of
usability. Subjective measures assess impression of
the customers towards the design of the Web site
as well as the effect of the Web site design towards
customer interaction.
Studies had found that checklist data can be both
reliable and valid for the assessment of user satisfaction
with Web sites or computer-based applications [19].
B. Artificial Neural Networks
Artificial Neural Networks (ANN) consist of a
parallel collection of simple processing units
(neurons/nodes) arranged and interconnected in a
network topology [47]. ANN inspired by b iological
nervous system, are known as parallel d istributed
processing (PDP) systems. ANN consists of a set of
interconnected processing units (node, neurons or cells).
A Glowworm Optimization Method for the Design of Web Services 91
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
Each node has activation functions. The activation
signal sent (output) by each node to other nodes travel
through weighted connection and each of these nodes
accumulates the inputs it receives, producing an output
according to an internal activation function. ANN is
closely related to its architecture and weights.
Multilayer architecture of network can be used to solve
both classification and function appro ximation
problems [42]. There are two types of learn ing networks
which are supervised learning and unsupervised or self-
organizing. Supervised learn ing is when the input and
desired output are provided while for unsupervised
learning, only input data is provided to the network.
The most popular supervised learning technique in
ANN is the back propagation (BP) algorithm [39]. Its
learning consists of the following steps :
1 An input vector is presented at the input layer.
2 A set of desired output is presented at the
output layer.
3 After a forward pass is done, the errors
between the desired and actual output are
compared.
4 The comparison results are used to determine
weight changes (backwards) according to the
learning rules.
In order to get the desired output from ANN, the
output from the network is compared to actual desired
output. During training, the network tries to match the
outputs with the desired target values. Network need to
review the connection weight to get the best output. The
idea of the BP is to reduce this error, until the ANN
learns the training data. The training begins with
random weights, and the goal is to adjust them so that
the learning erro r will be at minimal. ANN nodes in BP
algorithm are organized in layers, send their signals
forward and then the learning error (difference between
actual and expected results) is calculated and
propagated backwards until met satisfactory learning
error.
C. Glowworm Swarm Optimization
In GSO [4] a swarm is composed of N agents called
glowworms. A state of a glowworm i at time t can be
described by the following set of variab les: a position in
the search space(xi(t)), a luciferin level (l
i(t)) and a
neighbourhood range (ri(t)). GSO algorithm describes
how these variables change over time.
Initially, agents are randomly d istributed in the
search space. Other parameters are init ialized by
predefined constants. Each, next iterat ion is composed
of three phases: luciferin level update, glowworm
movement and neighbourhood range update.
To encode in the luciferin level the fitness of the
current position of a glowworm i, the fo llowing formula
is used:
li(t) = (1 − ρ)l
i(t − 1) + γJx
i(t) (1)
where: ρ is the luciferin decay constant, γ is the
luciferin enhancement constant and J is an objective
function.
Then, each glowworm tries to find neighbours. In
GSO a glowworm j is a neighbour of a glowworm i
only if the distance between glowworms i and j is
shorter than the neighbourhood range ri(t) and
additionally glowworm j has to shine brighter than i
(lj(t) > l
i(t)). If one glowworm has multip le neighbours,
chooses one by random with probability proportional to
the luciferin level of this neighbour. Finally, glowworm
moves one step in direction of the chosen neighbour.
Step size is constant and equals s.
In the last phase, the neighbourhood range ri(t) is
updated in order to limit the range of the
communicat ion in an ensemble of agents. The following
formula is used:
ri(t + 1) = min rs, max [0, r
i(t) + β(nd − |ni(t)|)] (2)
where: rs is a sensor range (a constant, which limits the
size of the neighbourhood range), nd is a desired number
of neighbours, |ni(t)| is a number of neighbours of a
glowworm i at time t, and β is a model constant.
D. eCommerce Usability and Quality
There are both unique and overlapping segments in
the fields eCommerce Usability, and eCommerce
Quality. Figure 2 presents the areas that are of primary
concern to this paper (12, 123 and 23).
13 23
123
121 2
3
eCommerce UsabilityWebsite Usability
eCommerce Quality
Fig. 1: Overlapping field segments
For defining eCommerce Quality and Usability
dimensions three eCommerce Quality scales (Refined
E-S-QUAL [5], WSSQ [6] and .comQ [45]) and three
eCommerce Usability scales (UFOS [9], W EBUSE [8]
and PSSUQ [1]) were selected. Their dimensions and
questions were compared and contrasted to produce
eCommerce scales covering both fields. The new
eCommerce usability scale consists of eleven
dimensions as presented in Figure 3. It is implemented
as a new checklist with eleven eCommerce dimensions
and 53 respective questions measured as the extent to
which participants agreed with statements on five-point
Likert scales, ranging from “Strongly Disagree” to
“Strongly Agree” [14].
92 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
Availability
Website performance
Personalization
Accessibility
Self-descriptiveness
Ordering/ Buying
Security/Privacy
Efficiency
Enjoyment/Satisfaction
LearnabilityError Handling
Personalization
Enjoyment
Efficiency
Privacy
Availability
Extracted eCommerce Quality Dimensions Extracted eCommerce Usability Dimensions
Accessibility of general conditions
Handling of shopping cart/ordering process
Self-descriptiveness
Learnability
Errors/System Reliability
Performance and Effectiveness
Fig. 2: Resulting eCommerce usability dimensions
In the fo llowing the dimensions and two example
questions of each dimensions of the checklist are given:
Dimension 1 – Efficiency
o This ecommerce Web site makes it easy for me
to find what I need/what I am looking for.
o The eCommerce Web site enables me to
complete a transaction quickly.
Dimension 2 - Web site performance
o I am able to access eCommerce Web site quickly.
o I need not wait too long to open a page.
Dimension 3 – Availability
o This eCommerce Web site does not crash.
o Pages at this eCommerce Web site do not freeze
after I enter my order information.
Dimension 4 – Learnability
o It was easy to learn to use the eCommerce Web
site.
o The information provided by the eCommerce
Web site was easy to understand.
Dimension 5 – Accessibility
o I easily find useful information about the terms
of delivery.
o I easily find how my personal data is being
handled.
Dimension 6 – Ordering/Buying
o It is easy to put products into the shopping cart.
o I always know which products are in the
shopping cart.
Dimension 7 – Self-descriptiveness
o When I click on something, I find what I expect.
o I always know what actions are available to me.
Dimension 8 – Error Handling
o The eCommerce Web site gives error messages
that clearly tell me how to fix problems.
A Glowworm Optimization Method for the Design of Web Services 93
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
o Whenever I make a mistake using the
eCommerce Web site, I recover easily and
quickly.
Dimension 9 – Personalization
o The eCommerce Web site understands my
specific needs.
o The eCommerce Web site offers me ext ra
services or information based on my preferences.
Dimension 10 – Security/Privacy
o The eCommerce Web site has adequate security
features.
o I feel like my privacy is protected at the
eCommerce Web site.
Dimension 11 – Enjoyment/Satisfaction
o I feel happy when I use the eCommerce Web site.
o The eCommerce Web site has an attractive
appearance.
Efficiency
Effectiveness
Satisfaction
Usability
Index
Integrated
Index
Efficiency
Website
performance
Availability
Learnability
Accessibility
Ordering/Buying
Self-descriptiveness
Error Handing
Personalization
Security/Privacy
Enjoyment/Satisfaction
Time
Taken
# Of
Errors
%
Complete
Q53
Q1:Q13
Q14:Q18
Q19:Q20
Q21:Q22
Q23:Q28
Q29:Q35
Q36:Q39
Q40:Q41
Q42:Q44
Q45:Q48
Q49:Q52
Usability Index Calculation Integrated Index Calculation
Fig. 3: GSO usability evaluation model
94 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
In GSO usability evaluation model the entire
construct of usability is represented by a single
dependent variable [22]. Two different quantitative
indices are calculated for the usability evaluation of
eCommerce systems, as shown in Figure 4.
Dim 3
Integrated
index
Dim 4 Dim 5 Dim 6 Dim 7 Dim 8 Dim 9Dim 2Dim 1Dim
10
Dim
11
Q1:Q13 Q14:Q18 Q19:Q20 Q21:Q22 Q23:Q28 Q29:Q35 Q36:Q39 Q40:Q41 Q42:Q44 Q45:Q48 Q49:Q52
Input(s)
Output
Input(s) Input(s) Input(s) Input(s) Input(s) Input(s) Input(s) Input(s) Input(s) Input(s)
Fig. 4: Neural network structure
The usability index was calculated using the
efficiency, effectiveness and satisfaction dimensions.
Both objective and subjective measures of eCommerce
usability are gathered. The objective data (efficiency
and effectiveness) are measured as follows [11].
For each task, eCommerce efficiency was measured
using:
1. Time taken to perform a task:
2. Number of errors made while performing the
task
For each task, eCommerce effectiveness is measured
using the percentage of the task solved. Satisfaction was
measured by customer‟s subjective response to a
question. The weights of the dimensions of usability
index efficiency, effectiveness and satisfaction are
determined by principal component analysis. The
integrated index uses eleven eCommerce usability and
eCommerce quality dimensions.
The most crit ical usability dimensions / checklist
items are adaptively determined by the weights of the
neural network GSO model. Depending on the
problems that these dimensions/questions indicate
relevant design improvements of eCommerce system
are proposed.
III. Case Study
A. Website and User Tasks
For experimental implementation and study of GSO
method a prototype retail eCommerce website (cf.
Figures 5-7) was created together with the biggest
anthurium producing company in Caribbean, Kairi
Blooms Ltd. 66 BSc students at the University of the
West Indies participated in the study. The percentage of
females taking part in the testing was 55 percent, with
45 percent being male. Year one students was the
largest test group, and this was useful for testing
purposes, because they represented the most
inexperienced computer users. The students solved the
following three tasks using the eCommerce system:
Task 1 - You are owner of an US flower shop and
want to buy anthuriums for your shop. You decide to
obtain the price for 30 Large “Bright Red” Anthurium
flowers. After v iewing the total cost you change your
mind and want to buy 20 Small “Light Pink” flowers.
Place your order without registering. Enter your contact
informat ion, for example, name: John Doe, telephone:
1-347-650-2388, ext 321, email: john_doe@gmail.com.
Your shipping informat ion is: .... Your billing
informat ion is: .... You pay by your credit card,
American Express: ...
Task 2 - Register with the Kairi Blooms Web site.
Enter your own name and email. Your shipping
A Glowworm Optimization Method for the Design of Web Services 95
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
informat ion is: ...You decide to buy the following
flowers:..... Place 25 Small “Light Green” flowers in
your Shopping List. After seeing the total cost, instead
of the 55 Large “Dark Pink” flowers buy 30 Small
“Light Pink” flowers and additional 20 Min i “Light
Red” flowers. You want the flowers to be delivered the
next day. Pay by your credit card, Visa:...Order these
flowers.
Task 3 - Buy a box of medium sized anthuriums from
the Web site. The box consists of the following
flowers:... Instead of 15 Off White, buy 10 Bright Red
and 5 Orange blooms. You now decide to change the
size of flowers to Small and buy a box of 70 flowers.
You place 35 Light Pink flowers and 35 Dark Red
flowers in this box to obtain an average Unit Shipping
Cost of $US 7.63. Buy five of these boxes. Your
shipping informat ion is: ...You change your mind about
paying by credit card, and decide to pay cash. You want
the flowers delivered by DHL Express to your home
after five days.
Fig. 5: Buy anthurium flowers
Fig. 6: Buy anthurium box
96 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
Fig. 7: View cart
For each of these three tasks the time and number of
errors for performing the task and the percentage of
tasks solved is recorded. The time taken to complete the
tasks and checklist was on average 50-60 minutes per
student. On Table 1 is shown some statistics based on
the data collected.
Table 1: Mean and standard deviations of data measuring efficiency and effectiveness
Dimensions Measure Task 1 Task 2 Task 3 Total
Time [min] Maximum 25 25 26 57
Minimum 3 3 2 13
Mean 11 13 10 35
Standard Deviation 5.40 4.82 4.74 9.00
% of solved task Maximum 100 100 100 300
Minimum 14 13 15 120
Mean 89 90 86 268
Standard Deviation 13.69 12.93 21.02 36.00
Errors Maximum 12 10 10 28
Minimum 0 0 0 0
Mean 2 2 2 6
Standard Deviation 2.26 1.97 2.26 5.00
By principal component analysis the weights of the
dimensions of usability index efficiency, effectiveness
and satisfaction were found as 0.20, 0.34 and 0.46
respectively.
B. GSO Results
The GSO algorithm used operates by evaluating a
multi-input single-output (MISO) cost function. The
cost function places values on each particle for ranking
within the GSO algorithm. The optimizat ion cost
function accepted 52 input parameters and returned a
single output parameter, representing the integrated
index. For each data set the inputs was represented as:
Integrated-Index = f(V1, V2, …, V52), where Vij =
response for question i of user j. On the left hand side of
the equation the „Integrated Index‟ term is a scalar value
but represents a weighted sum of many indiv idual
hidden calculated terms. The function is able to meet
the objectives of minimizing d istance to target and
reducing end velocity by using these hidden terms. The
goal is to optimize a set of functions F(x) subject to a
set of constraints C. Each function within is
multip lied by a weight alpha and summed over the size
A Glowworm Optimization Method for the Design of Web Services 97
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
of . The MISO can thus be solved using the
standard weighed sum of objectives.
The problem space, for each epoch during the
training of the neural network, is seeded with a
population of particles (checklist items, ) over the
range of interest. Each particle‟s position in hyperspace
represents a candidate problem solution. The
optimization task was for the adjustment of the layer
weights to get as close as possible to the targeted
integrated index. Preceding the task the following
parameters were controlled:
a. Number of particles (52)
b. Search range ( for each dimension was set to
[0,1] )
c. Error goal (network output versus integrated
index)
d. Seeding agent initial positions (user
responses,Vi)
e. Plotting and display options
f. Iterations to train (maximum set at 2000)
g. Minimization strategy (1e-0005)
For change detection in this GSO, the global best is
polled every ten iterations to see if the position has
remained the same depending on the number of disjoint
groups that occurs during swarm format ion. Each agent
is updated according to the luciferin level update,
glowworm movement and neighbourhood range update.
Reducing the error goal using the min imization strategy
was the optimizat ion target for the GSO. The stopping
criterion was based on the speed of convergence to this
desired error goal. On Figures 8 and 9 are shown how
long the algorithm took to converge to an acceptable
optimal solution, using different data sets, by showing
changes to the global best as training progressed.
Fig. 8: GSO training performance for N=12 data set
Fig. 9: GSO training performance for N=34 data set
The final question weights and layer weights were
recorded for the training set that showed most promise,
that is, the lowest mean squared error (MSE). As can
be seen on Figure 8 and 9 the algorithm converged to a
desired solution faster for the N=34 data set.
C. GSO Results Analysis
The analysis done on the GSO Approaches was
guided by the fact that the neural network used for the
algorithms was partially connected, Figure 3. In effect
the bottom-up checklist item, d imension and usability
index calcu lations were similar to the summat ion effect
of the transfer function that was used during neural
network processing. The calculat ion of the usability
index was the target value.
The average value for each checklist item was
obtained. The result of the neural network processing
from either approach was item and dimension weights.
Using these weight-value pairs a value was calculated
for the relevant dimension. In a similar fashion the
dimension values and weight pairs were combined to
produce the usability index. The value of the usability
index was 2.35. The average value of the integrated
indices obtained from each test participant was 2.47, the
calculated usability index.
While there is substantial literature on how to
conduct usability evaluations (for example, [29], [37],
[12], [27], [34], little attention has been paid to the way
that usability evaluations lead to recommendations for
changes. This is a crit ical step in making sure that the
results of evaluations have an appropriate impact on
product development. If the translation from problem
to solution is flawed, or if the recommendations are not
taken seriously by the product team, a usability
evaluation is a costly step that may have little impact on
the product [13]. The severity ratings of usability
problems were defined [30] and adapted as follows:
98 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
Table 2: Severity Rating for Usability checklist items
Rating Description Range Color
None no observed usability problems
Dark Green
Cosmetic the usability problem delayed users slightly
Light Green
Serious the usability problem was one that slowed down users significantly but did allow them to complete their task
Yellow
Catastrophe a usability problem that prevented the user from completing a task
Red
Obtaining the dimensions and checklist items which
contributed the most to the usability index was of
primary concern at this stage of the project. As
displayed on Figure 12, the checklist item, dimension
and usability index value(s) were calcu lated and rated
according to the scale by Nielsen [30]. The resulting
ratings were used to suggest improvements to the
system (cf. Figure 10 and 11).
4
1
No
3
5
6
Vi(0)
Î [0, 1) ? Vi(0)
Î [1, 2) ? Vi(0)
Î [2, 3) ? Vi(0)
Î [3, 4] ?
Catastrophic
Usability
Problem
Severe Usability
Problem
Cosmetic
Usability
Problem
No Usability
Problem
YesIs last value
found ?
Find value of 1st
checklist item - Vi(0)
Î [0, 4]
Choice of neural network algorithm
Neural network weights learning
Standardization of weights for checklist items
and dimension. For each group:
∑ Wi = 1 , WiÎ [0, 1]
Find value of 1st
checklist dimension –
Vi(1)
Î [0, 4]
No
Vi(1)
Î [0, 1) ? Vi(1)
Î [1, 2) ? Vi(1)
Î [2, 3) ? Vi(1)
Î [3, 4] ?
Catastrophic
Usability
Problem
Severe Usability
Problem
Cosmetic
Usability
Problem
No Usability
Problem
YesIs last value
found ?
Find value of Usability Index –
Iu Î [0, 4]
Iu Î [0, 1) ? Iu Î [1, 2) ? Iu Î [2, 3) ? Iu Î [3, 4] ?
Catastrophic
Usability
Problem
Severe Usability
Problem
Cosmetic
Usability
Problem
No Usability
Problem
2
No No No No
No No No
No No No
Yes Yes Yes Yes
Yes Yes Yes Yes
Yes Yes Yes Yes
Fig. 10: GSO Value Analysis
A Glowworm Optimization Method for the Design of Web Services 99
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
4
1
No
3
5
Is Vi(0)
³ 3 ?
Suggest
Usability
Improvement –
Vi(0)
< 3
Yes
Is last value
checked ?
Check value of 1st
checklist item –
Vi(0)
Î [0, 4]
Sort weighted values for checklist dimensions (Viw(1))
and items (Viw(0))
Select dimension with highest weighted value (Viw(1))
and check it‟s value Vi(1)
No
Yes
Select next checklist item with
highest weighted value (Viw(0))
2
No
YesYes
No
Is Usability Index
Iu < 2 ?
Is
Vi(1)
³ 2 ?
Is estimated
improvement for item
< 1% of
Usability Index?
Is Usability
Index
Iu < 2 ?
Select dimension item with highest weighted value
(Viw(0))
Is last weighted item
value (Viw(0)) of
dimension checked ?
Select next dimension with highest weighted value
(Viw(1)) and check it‟s value Vi
(1)
Select value (Vi(0)
) of next
checklist item
Suggest Usability Improvement –
Vi(0)
< current Vi(0)
Yes
Yes
Yes
Satisfactory Usability Indication
No
NoNo
Reject
Improvement
Accept
Improvement
Figure 11: GSO Usability Estimation Improvement Algorithm
The steps of this algorithm were fo r the GSO method.
The most critical dimensions affecting eCommerce
usability using the GSO Approach were:
security/privacy
personalization
website performance
error handling
enjoyment/satisfaction
For any usability analysis, expert opinion is essential.
Because of this, the results obtained by the two
approaches, will assist the expert in analyzing important
usability areas to focus on for a given system. The
reduction in the number of d imensions and eventually
questionnaire items can be compared to a backtracking
search algorithm (the expert is guided by the results as
to which items are more important, when considering
the usability of the system). W ith reduced number of
100 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
items/dimensions to evaluate, the eventual evaluation of
the system will be faster.
IV. Conclusions
A method for adaptive usability evaluation of
eCommerce systems GSO was proposed. It includes a
checklist and a neural networks-based model for
evaluation of eCommerce usability. A case study
confirmed GSO applicab ility for measuring and
allocation of usability problems. The advantages of the
approach are: (1) measuring by GSO checklist of both
usability and quality of eCommerce systems; (2)
adaptive selection of most significant usability
dimensions and items and thus significant reduction of
the time for usability evaluation and design.
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Koffka Khan was born in San
Fernando, Trinidad and Tobago in
1978. He received the B.Sc. and
M.Sc. degrees from University of
the West Indies, in 2002 and 2008,
respectively. He was awarded by
the University of the West Indies for his contributions
made in postgraduate work in 2009 as a research
assistant.
He is working presently at The University of The
West Indies; St. Augustine Campus (TRINIDAD &
102 A Glowworm Optimization Method for the Design of Web Services
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 10, 89-102
TOBAGO) as a Tutor in Computer Science in the
Department of Computing and Information Technology
(Faculty of Science & Agriculture) since September
2006. Mr. Khan started his teaching-n-research career
as a Demonstrator in Computer Science at the
University of The West Indies at the Department of
Mathematics and Computer Science. He has up-to-date,
published ten research and co-authored four papers in
journals of international repute & in the proceedings of
international conferences.
Ashok Sahai is working presently at
The University of The West Indies;
St. Augustine Campus (TRINIDAD
& TOBAGO) as a Professor of
Statistics in the Department of
Mathematics & Statistics (Faculty of
Science & Agriculture) since
February 2006. Dr. Sahai started his
teaching-n-research career as a Lecturer in Statistics
Department at Lucknow University (INDIA) in Ju ly
1966, and continued thereat till April 1980. He has, up-
to-date, published more than one hundred research
papers in peer-reviewed journals of international repute
& in the peer-rev iewed proceedings of international
conferences.
He worked as Reader in Statistics and as Professor of
Statistics in the Department of Mathematics at
University of Roorkee (Now IIT Roorkee) during the
period: April 1980- July 1995. Prof. Sahai had also
worked as an Assoc. Professor of Statistics at
University of Dar-Es-Salaam; TANZANIA (East Africa)
during the period: July 1982- June 1984, and as a
Professor of Statistics at University of Swaziland
(Southern Africa) during the period: Ju ly 1993- June
2003. He worked as a Guest Scholar @
PharmacoEconomic Research Centre; University of
Arizona, TUCSON during the period from July 2003 to
October 2003 and as Visit ing Professor @ Hyderabad;
INDIA during December 2003 to January 2006 in
ICFAI Tech. University, Medchel Rd.; in Aurora
School of Management at Chikkadpally; and in St.
Ann‟s P.G. (Management) College For Women at
Mallapur.
How to cite this paper: Koffka Khan, Ashok Sahai,"A
Glowworm Optimization Method for the Design of Web Services", International Journal of Intelligent Systems and
Applications(IJISA), vol.4, no.10, pp.89-102, 2012. DOI:
10.5815/ijisa.2012.10.10
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